Skills 导航

Use the MemOS Lite memory system to search and use the user's past conversations. Use this skill whenever the user refers to past chats, their own preferences or history, or when you need to answer from prior context. When auto-recall returns nothing (long or unclear user query), generate your own short search query and call memory_search. Use task_summary when you need full task context, skill_get for experience guides, and memory_timeline to expand around a memory hit.

Set up the full OpenClaw agent memory system with 3-tier memory (HOT/WARM/COLD), daily logs, semantic search (QMD), and lossless context management (Lossless Claw). Use when onboarding a new agent, setting up memory for a fresh OpenClaw instance, or when asked to install the memory system on a new agent. Triggers on "set up memory", "install memory system", "onboard new agent memory", "memory setup", "agent onboarding".

Stop agents from "forgetting, mixing projects, and rotting over time" by giving them a practical memory operating system: global memory, project memory, promotion rules, validation cases, and a maintenance loop.

Long-term memory, learning, and self-evolution for the agent. Activates on session start (SOUL.md/USER.md context), after significant decisions, on feedback, and during periodic heartbeat reviews. Maintains MEMORY.md, daily logs, learnings corpus, and behavioral patterns.

This skill should be used when working with memory-lancedb-pro, a production-grade long-term memory MCP plugin for OpenClaw AI agents. Use when installing, configuring, or using any feature of memory-lancedb-pro including Smart Extraction, hybrid retrieval, memory lifecycle management, multi-scope isolation, self-improvement governance, or any MCP memory tools (memory_recall, memory_store, memory_forget, memory_update, memory_stats, memory_list, self_improvement_log, self_improvement_extract_skill, self_improvement_review).

Systematic memory management for long-running AI agents. Implements a five-tier lifecycle — heartbeat micro-attention, nightly consolidation, weekly reflection, monthly archiving, and yearly wisdom distillation. Use when setting up a new agent's memory system, improving an existing agent's memory quality, or when the agent's MEMORY.md is growing too large and context quality is degrading. Triggers on "set up memory", "memory management", "improve memory", "memory lifecycle", "nightly consolidation", "sleep cycle", "memory housekeeping".

记忆管理技能 - 五层时间架构 + 三类记忆标签 + 最小化写入 + 压缩检测

多实例记忆共享，多个 Agent 之间同步记忆

Integración con MemPalace para gestión de memoria semántica persistente. Use when you need to search, add, or query memories stored in a MemPalace palace. Supports semantic search, knowledge graph queries, and memory management via ChromaDB-based storage.

对话记忆仓库：自动归档 session 对话，保留原始记录，支持检索和误解纠正。可与 memory-never-forget 联动形成完整记忆体系。

Emotional processing layer for AI agents. Persistent emotional states that influence behavior and responses. Part of the AI Brain series.

记忆管理技能 - 三层时间架构 + 自动搜索/提取/会话笔记统一 Hook + 记忆巩固（整合 auto-dream）

AI 记忆栈架构 - 符合 2026 前沿的 AI 记忆系统。微调+RAG+ 上下文三层设计，mirrors 人类记忆工作方式。

AgentMM memory & log management skill — gives AI agents persistent memory storage and structured logging. Use when the user asks to remember information, recall memories, or record/query logs. Requires env var AGENTMM_API_KEY (format: amm_sk_xxx).

Persistent local cognitive memory for OpenClaw via a Node adapter and FastAPI engine.

Structured persistent memory system using an Obsidian vault with daily journals, project docs, knowledge base, and self-improvement logging. Use when: (1) Setting up a new OpenClaw agent's memory system, (2) Agent needs persistent memory across sessions, (3) Organizing project documentation, daily journals, or knowledge base, (4) Logging errors, learnings, or feature requests for continuous improvement, (5) User says 'set up memory', 'initialize vault', 'create journal', 'log this error', 'remember this', or 'update memory'. Also covers semantic search setup and the promotion pipeline for learnings into brain files.

An Agent long-term memory guide for OpenClaw, Codex, and Obsidian workflows. Covers MEMORY.md, daily notes, session recovery, memory distillation, and optional OpenViking support.

Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.

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Memory Palace for AI — persistent structured memory, identity, and task management across every conversation.

TruContext persistent memory for OpenClaw agents. Use when you need to remember something significant across sessions, recall prior context, query the knowledge graph, check what TC is curious about, or declare entity nodes. Triggers on: 'remember this', 'recall what we know about', 'check TC', 'what has TC flagged', 'create a node for', 'find the node for'.

基于 LanceDB 向量数据库的智能记忆系统，为 OpenClaw Agent 提供长期记忆和语义检索能力。

Use the MemOS Local memory system to search and use the user's past conversations. Use this skill whenever the user refers to past chats, their own preferences or history, or when you need to answer from prior context. When auto-recall returns nothing (long or unclear user query), generate your own short search query and call memory_search. Available tools: memory_search, memory_get, memory_write_public, task_summary, skill_get, skill_search, skill_install, skill_publish, skill_unpublish, memory_timeline, memory_viewer.